The present invention relates to an automated system and method for generating learned models of behavior of an actor and/or the actor's environment. More particularly, it relates to a system and method that generates learned models of behavior relating to particular actor/environment events and activities for use in adapting operations of an automated human actor environment monitoring system that may include response capabilities.
The evolution of technology has given rise to numerous, discrete devices adapted to make actor environment more convenient. For example, in the realm of in-home environments, companies are selling microwaves that connect to the internet, and refrigerators with computer displays, to name but a few. These and other advancements have prompted research into the feasibility of a universal home control system that not only automates operation of various devices or appliances within the home, but also monitors activities of an actor in the home. It could then perform device control based upon the actor's activities and/or events in the living space. In other words, it may now be possible to provide coordinated, situation-aware, universal support to an actor in a daily living environment.
The potential features associated with the “intelligent” home described above are virtually limitless. By the same token, the extensive technology and logic obstacles inherent to many desired features have heretofore prevented their implementation. In general terms, the automated monitoring and response system will consist of a suite of sensors in the living space to detect actor and environment states, a set of actuators that control devices in the environment as well as facilitate communication with the actor (and others), and a computer system (local or remote) capable of making decisions to assist the actor. With these parameters in mind, current response and monitoring systems rely on a pre-programmed computer system that executes predetermined operations based upon a predetermined number and located sensors and actuators within the actor's home, each sensor and actuator having known capabilities. These systems are highly inflexible. From a market viability standpoint, such a configuration is simply impractical. That is to say, virtually every installation of the monitoring and response system will entail a different environment layout, and varying numbers and capabilities of sensors and actuators. An inflexible control system is simply unable to adapt to varying sensor and actuator configurations, and thus is of reduced value. Along these same lines, currently-envisioned automated monitoring and response systems rely upon a highly labor intensive initial configuration, whereby the actor and/or others are required to personally “set-up” the operational parameters (e.g., designate types, locations, and capabilities of sensors and actuators; designate typical or expected daily activities of the actor; designate daily needs of the actor; etc.). Similarly, the capabilities, needs and responses provided by the system will almost certainly need to change over time due to changes in the actor and/or the actor's environment. Currently, inflexible systems require that the actor and/or others not only independently recognize that a change in the actor's needs or activities has occurred, but also that the system be manually re-programmed to appropriately address the change(s). For a “technophobic” actor, the initial configuration is overwhelming at best, as would be any subsequent system reconfiguration to address inevitable changes in the actor or in the actor's environment.
Emerging sensing and automation technology represents an exciting opportunity to develop an independent in-home assistant system. In this regard, a highly desirable attribute associated with such a system is an ability to be rapidly deployable, easy to configure, and automatically adapt as the actor, the actor's environment, and technology changes. Unfortunately, current techniques for configuring an appropriate system are highly inflexible, and difficult to configure. Thus, a need exists for a system and method for learning properties (e.g., patterns, profiles, preferences, etc.) of an actor or their environment (including other humans/animals in the environment) as part of the operation of an automated actor environment monitoring and response system.